Automated recognition of the behavior of robots is increasingly needed in a variety of tasks, as we develop more autonomous robots and general information processing agents. For example, in environments with multiple autonomous robots, a robot may need to make decisions based on the behavior of the other robots. As another interesting example, an intelligent narrator agent observing a robot will need to automatically identify the robot's behaviors. In this paper, we introduce a novel framework for using Hidden Markov Models (HMMs) to represent and recognize strategic behaviors of robotic agents. We first introduce and characterize the perceived signal in terms of behavioral-relevant state features. We then show how several HMMs capture different defined robot behaviors. Finally we present the HMMbased recognition algorithm which orchestrates and selects the appropriate HMMs in real time. We use the multi-robot robotic soccer domain as the substrate of our empirical validation, both in simulation and using real robots. Robots will then adapt their behaviors as a function of the autonomously recognized behavior of the other agents, either teammates or opponents.

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Robotic soccer is a challenging research domain which involves multiple agents that need to collaborate in an adversarial environment to achieve specific objectives. In this paper, we describe CMUnited, the team of small robotic agents that we developed to enter the RoboCup-97 competition. We designed and built the robotic agents, devised the appropriate vision algorithm, and developed and implemented algorithms for strategic collaboration between the robots in an uncertain and dynamic environment. The robots can organize themselves in formations, hold specific roles, and pursue their goals. In game situations, they have demonstrated their collaborative behaviors on multiple occasions. The robots can also switch roles to maximize the overall performance of the team. We present an overview of the vision processing algorithm which successfully tracks multiple moving objects and predicts trajectories. The paper then focusses on the agent behaviors ranging from low-level individual behaviors to coordinated, strategic team behaviors. CMUnited won the RoboCup-97 small-robot competition at IJCAI-97 in Nagoya, Japan.Content Areas: autonomous robots; multi-agent teams; coordinating perception, thought, and action; multi-agent communication, coordination, and collaboration; real-time performance. £We thank Sorin Achim for developing and building the robots.

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